E.g. data infrastructure, declarative tooling for experiments, visualisation tooling, data sharing/exchange services, provenance infrastructure for threading data generation through to ML model training, protocols for lab equipment, etc.

So I'm wondering if people on the more traditional side of tech are interested in building devtools/infra for science, what you see as the main barriers, whether there's anything specific you'd like to build, and what would help you get started?

For those working in science, I'm also curious what tools you'd like to see built, and whether you'd be willing to collaborate with an engineer to make them happen?

Comments URL: https://news.ycombinator.com/item?id=48295946

Points: 3

# Comments: 3

Code: https://github.com/opensciencearchive/server.

Website: https://opensciencearchive.org/

Two demos:

https://pockets.bio

https://lingual.bio

I've got demos up and running (mirroring/extending PDB and GEO). Next I'm working on APIs with good AX, ML-friendly export, and an unified AI-driven UI that works for all scientific data types.

If anything, it's surprising that English-speaking countries like it so much.

>I thought only the US ranks it high

Joyce never even set foot in the United States... You could say this about The Great Gatsby, which US sources might rank in the top 5 compared to 46 in this list.

In Dublin's best music venues, nobody is singing along because it's brand new material from brand new artists. If you're singing along to well known songs in Temple Bar then I'm afraid you're missing some of the best music the city has to offer, in venues like Whelan's, Workmans, Sin É, The Grand Social etc.

“Protein Data Bank-in-a-box”

https://opensciencearchive.org

“Protein Data Bank-in-a-Box”

https://opensciencearchive.org

Many people advise spending about a year doing more sets of fewer (~5) reps to build strength, and then switch to fewer sets of more reps (8-12) when you want to build muscle mass.

Point being, the idea of doing lighter weights until failure is already kind of there in body building wisdom.

It is strength training (not body builder) wisdom to use heavy weights with few reps. Hypertrophy (i.e. body builder) programmes usually call for 8-12 reps, which implies relatively low weights.

The arbiters are just our colleagues, at the end of the day. The journal is just the organisational mechanism, one of many possible mechanisms.

For example, I follow a weekly reading list (https://superlab.ca) published by a group of motor control labs at Western University. Those people are my arbiters of quality.

I want to continue having arbiters, and I want it to be the same people (broadly speaking). I just don't want them to be organised around journals because journals are toxic and lead to concentrated power over scientific narratives.

Great question.

> the journal system isn't perfect but its the only real check we have left.

I wish I could agree but Nature et al continually publish bad, attention-grabbing science, while holding back the good science because it threatens the research programmes that gave the editorial board successful careers.

"Isn't perfect" is a massive understatement.

Journals should either become tech companies offering (and charging for) new and exciting ways to present scientific research, or simply stop existing.

1. Journals want to publish lots of articles, so they are incentivised to provide a better publishing experience to authors (i.e. better tech, post-PDF science, etc) - Good.

2. Journals will stop prioritising quality, which means they will relinquish their "prestige" factor and potentially end the reign of glam-journals - Good.

3. Journals will stop prioritising quality, which means we can move to post-publication peer-review unimpeded - Good.